Yong-Jin Park, Sang Won Seo, Seong Hye Choi, So Young Moon, Sang Joon Son, Chang Hyung Hong, Young-Sil An
{"title":"使用早期F-18氟替他莫PET预测淀粉样蛋白阳性的机器学习。","authors":"Yong-Jin Park, Sang Won Seo, Seong Hye Choi, So Young Moon, Sang Joon Son, Chang Hyung Hong, Young-Sil An","doi":"10.1177/13872877251351275","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting amyloid positivity.ObjectiveThis study aimed to evaluate whether early-phase fluorine-18 flutemetamol (eFMM) PET images provide valuable information for predicting amyloid positivity using machine learning (ML) models and whether incorporating clinical and neuropsychological features improves predictive performance.MethodsIn total, 454 patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) were enrolled and randomly divided into training (n = 354) and test (n = 100) groups. We developed ML models using logistic regression (LR) and linear discriminant analyses (LDA) for predicting amyloid positivity: eFMM features alone (eFMM model), eFMM features combined with clinical features (eFMM + C model), eFMM features combined with neuropsychological features (eFMM + N model), eFMM features combined with both clinical and neuropsychological features (eFMM + C + N model), clinical and neuropsychological features combined (C + N model), and dFMM features alone (dFMM model).ResultsIn the test group, the eFMM models achieved areas under the receiver operating characteristic curves (AUROCs) of 0.791 (LR) and 0.779 (LDA). The eFMM + C + N models significantly improved predictive performance, with AUROCs of 0.902 for both LR and LDA, outperforming the eFMM models.ConclusionsML predictive models using eFMM PET data demonstrated fair performance in predicting amyloid positivity in patients with MCI and AD. The addition of relevant clinical and neuropsychological features further enhanced the predictive performance of the eFMM models, achieving excellent performance.</p>","PeriodicalId":14929,"journal":{"name":"Journal of Alzheimer's Disease","volume":" ","pages":"13872877251351275"},"PeriodicalIF":3.1000,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.\",\"authors\":\"Yong-Jin Park, Sang Won Seo, Seong Hye Choi, So Young Moon, Sang Joon Son, Chang Hyung Hong, Young-Sil An\",\"doi\":\"10.1177/13872877251351275\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting amyloid positivity.ObjectiveThis study aimed to evaluate whether early-phase fluorine-18 flutemetamol (eFMM) PET images provide valuable information for predicting amyloid positivity using machine learning (ML) models and whether incorporating clinical and neuropsychological features improves predictive performance.MethodsIn total, 454 patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) were enrolled and randomly divided into training (n = 354) and test (n = 100) groups. We developed ML models using logistic regression (LR) and linear discriminant analyses (LDA) for predicting amyloid positivity: eFMM features alone (eFMM model), eFMM features combined with clinical features (eFMM + C model), eFMM features combined with neuropsychological features (eFMM + N model), eFMM features combined with both clinical and neuropsychological features (eFMM + C + N model), clinical and neuropsychological features combined (C + N model), and dFMM features alone (dFMM model).ResultsIn the test group, the eFMM models achieved areas under the receiver operating characteristic curves (AUROCs) of 0.791 (LR) and 0.779 (LDA). The eFMM + C + N models significantly improved predictive performance, with AUROCs of 0.902 for both LR and LDA, outperforming the eFMM models.ConclusionsML predictive models using eFMM PET data demonstrated fair performance in predicting amyloid positivity in patients with MCI and AD. The addition of relevant clinical and neuropsychological features further enhanced the predictive performance of the eFMM models, achieving excellent performance.</p>\",\"PeriodicalId\":14929,\"journal\":{\"name\":\"Journal of Alzheimer's Disease\",\"volume\":\" \",\"pages\":\"13872877251351275\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Alzheimer's Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/13872877251351275\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Alzheimer's Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/13872877251351275","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.
BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting amyloid positivity.ObjectiveThis study aimed to evaluate whether early-phase fluorine-18 flutemetamol (eFMM) PET images provide valuable information for predicting amyloid positivity using machine learning (ML) models and whether incorporating clinical and neuropsychological features improves predictive performance.MethodsIn total, 454 patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) were enrolled and randomly divided into training (n = 354) and test (n = 100) groups. We developed ML models using logistic regression (LR) and linear discriminant analyses (LDA) for predicting amyloid positivity: eFMM features alone (eFMM model), eFMM features combined with clinical features (eFMM + C model), eFMM features combined with neuropsychological features (eFMM + N model), eFMM features combined with both clinical and neuropsychological features (eFMM + C + N model), clinical and neuropsychological features combined (C + N model), and dFMM features alone (dFMM model).ResultsIn the test group, the eFMM models achieved areas under the receiver operating characteristic curves (AUROCs) of 0.791 (LR) and 0.779 (LDA). The eFMM + C + N models significantly improved predictive performance, with AUROCs of 0.902 for both LR and LDA, outperforming the eFMM models.ConclusionsML predictive models using eFMM PET data demonstrated fair performance in predicting amyloid positivity in patients with MCI and AD. The addition of relevant clinical and neuropsychological features further enhanced the predictive performance of the eFMM models, achieving excellent performance.
期刊介绍:
The Journal of Alzheimer''s Disease (JAD) is an international multidisciplinary journal to facilitate progress in understanding the etiology, pathogenesis, epidemiology, genetics, behavior, treatment and psychology of Alzheimer''s disease. The journal publishes research reports, reviews, short communications, hypotheses, ethics reviews, book reviews, and letters-to-the-editor. The journal is dedicated to providing an open forum for original research that will expedite our fundamental understanding of Alzheimer''s disease.